{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image segmentation with CamVid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "from fastai.vision import *\n",
    "from fastai.callbacks.hooks import *\n",
    "from fastai.utils.mem import *\n",
    "from unet import DynamicUnet\n",
    "from torchsummary import summary\n",
    "\n",
    "path = untar_data(URLs.CAMVID)\n",
    "path.ls()\n",
    "\n",
    "path_lbl = path/'labels'\n",
    "path_img = path/'images'\n",
    "\n",
    "import torch\n",
    "torch.cuda.set_device(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car', 'CartLuggagePram', 'Child', 'Column_Pole',\n",
       "       'Fence', 'LaneMkgsDriv', 'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving', 'ParkingBlock',\n",
       "       'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk', 'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',\n",
       "       'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel', 'VegetationMisc', 'Void', 'Wall'], dtype='<U17')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fnames = get_image_files(path_img)\n",
    "fnames[:3]\n",
    "\n",
    "lbl_names = get_image_files(path_lbl)\n",
    "lbl_names[:3]\n",
    "\n",
    "img_f = fnames[0]\n",
    "img = open_image(img_f)\n",
    "img.show(figsize=(5,5))\n",
    "\n",
    "get_y_fn = lambda x: path_lbl/f'{x.stem}{x.suffix}'\n",
    "\n",
    "mask = open_mask(get_y_fn(img_f))\n",
    "mask.show(figsize=(5,5), alpha=1)\n",
    "\n",
    "src_size = np.array(mask.shape[1:])\n",
    "src_size,mask.data\n",
    "\n",
    "codes = np.loadtxt(path/'codes.txt', dtype=str); codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using bs=4, have 4849MB of GPU RAM free\n"
     ]
    }
   ],
   "source": [
    "size = src_size//2\n",
    "\n",
    "free = gpu_mem_get_free_no_cache()\n",
    "# the max size of bs depends on the available GPU RAM\n",
    "if free > 8200: bs=8\n",
    "else:           bs=4\n",
    "print(f\"using bs={bs}, have {free}MB of GPU RAM free\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = (SegmentationItemList.from_folder(path_img)\n",
    "       .split_by_fname_file('../valid.txt')\n",
    "       .label_from_func(get_y_fn, classes=codes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (src.transform(get_transforms(), size=size, tfm_y=True)\n",
    "        .databunch(bs=bs)\n",
    "        .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.show_batch(2, figsize=(10,7))\n",
    "\n",
    "# data.show_batch(2, figsize=(10,7), ds_type=DatasetType.Valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "name2id = {v:k for k,v in enumerate(codes)}\n",
    "void_code = name2id['Void']\n",
    "\n",
    "def acc_camvid(input, target):\n",
    "    target = target.squeeze(1)\n",
    "    mask = target != void_code\n",
    "    return (input.argmax(dim=1)[mask]==target[mask]).float().mean()\n",
    "metrics=acc_camvid\n",
    "# metrics=accuracy\n",
    "wd=1e-2\n",
    "learn = unet_learner(data, models.resnet34, metrics=metrics, wd=wd)\n",
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 112, 112]           9,408\n",
      "       BatchNorm2d-2         [-1, 64, 112, 112]             128\n",
      "              ReLU-3         [-1, 64, 112, 112]               0\n",
      "         MaxPool2d-4           [-1, 64, 56, 56]               0\n",
      "            Conv2d-5           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-6           [-1, 64, 56, 56]             128\n",
      "              ReLU-7           [-1, 64, 56, 56]               0\n",
      "            Conv2d-8           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-9           [-1, 64, 56, 56]             128\n",
      "             ReLU-10           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-11           [-1, 64, 56, 56]               0\n",
      "           Conv2d-12           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-13           [-1, 64, 56, 56]             128\n",
      "             ReLU-14           [-1, 64, 56, 56]               0\n",
      "           Conv2d-15           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
      "             ReLU-17           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-18           [-1, 64, 56, 56]               0\n",
      "           Conv2d-19           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-20           [-1, 64, 56, 56]             128\n",
      "             ReLU-21           [-1, 64, 56, 56]               0\n",
      "           Conv2d-22           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-23           [-1, 64, 56, 56]             128\n",
      "             ReLU-24           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-25           [-1, 64, 56, 56]               0\n",
      "           Conv2d-26          [-1, 128, 28, 28]          73,728\n",
      "      BatchNorm2d-27          [-1, 128, 28, 28]             256\n",
      "             ReLU-28          [-1, 128, 28, 28]               0\n",
      "           Conv2d-29          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-30          [-1, 128, 28, 28]             256\n",
      "           Conv2d-31          [-1, 128, 28, 28]           8,192\n",
      "      BatchNorm2d-32          [-1, 128, 28, 28]             256\n",
      "             ReLU-33          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-34          [-1, 128, 28, 28]               0\n",
      "           Conv2d-35          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-36          [-1, 128, 28, 28]             256\n",
      "             ReLU-37          [-1, 128, 28, 28]               0\n",
      "           Conv2d-38          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-39          [-1, 128, 28, 28]             256\n",
      "             ReLU-40          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-41          [-1, 128, 28, 28]               0\n",
      "           Conv2d-42          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-43          [-1, 128, 28, 28]             256\n",
      "             ReLU-44          [-1, 128, 28, 28]               0\n",
      "           Conv2d-45          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-46          [-1, 128, 28, 28]             256\n",
      "             ReLU-47          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-48          [-1, 128, 28, 28]               0\n",
      "           Conv2d-49          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-50          [-1, 128, 28, 28]             256\n",
      "             ReLU-51          [-1, 128, 28, 28]               0\n",
      "           Conv2d-52          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-53          [-1, 128, 28, 28]             256\n",
      "             ReLU-54          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-55          [-1, 128, 28, 28]               0\n",
      "           Conv2d-56          [-1, 256, 14, 14]         294,912\n",
      "      BatchNorm2d-57          [-1, 256, 14, 14]             512\n",
      "             ReLU-58          [-1, 256, 14, 14]               0\n",
      "           Conv2d-59          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-60          [-1, 256, 14, 14]             512\n",
      "           Conv2d-61          [-1, 256, 14, 14]          32,768\n",
      "      BatchNorm2d-62          [-1, 256, 14, 14]             512\n",
      "             ReLU-63          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-64          [-1, 256, 14, 14]               0\n",
      "           Conv2d-65          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-66          [-1, 256, 14, 14]             512\n",
      "             ReLU-67          [-1, 256, 14, 14]               0\n",
      "           Conv2d-68          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-69          [-1, 256, 14, 14]             512\n",
      "             ReLU-70          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-71          [-1, 256, 14, 14]               0\n",
      "           Conv2d-72          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-73          [-1, 256, 14, 14]             512\n",
      "             ReLU-74          [-1, 256, 14, 14]               0\n",
      "           Conv2d-75          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-76          [-1, 256, 14, 14]             512\n",
      "             ReLU-77          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-78          [-1, 256, 14, 14]               0\n",
      "           Conv2d-79          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-80          [-1, 256, 14, 14]             512\n",
      "             ReLU-81          [-1, 256, 14, 14]               0\n",
      "           Conv2d-82          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-83          [-1, 256, 14, 14]             512\n",
      "             ReLU-84          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-85          [-1, 256, 14, 14]               0\n",
      "           Conv2d-86          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-87          [-1, 256, 14, 14]             512\n",
      "             ReLU-88          [-1, 256, 14, 14]               0\n",
      "           Conv2d-89          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-90          [-1, 256, 14, 14]             512\n",
      "             ReLU-91          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-92          [-1, 256, 14, 14]               0\n",
      "           Conv2d-93          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-94          [-1, 256, 14, 14]             512\n",
      "             ReLU-95          [-1, 256, 14, 14]               0\n",
      "           Conv2d-96          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-97          [-1, 256, 14, 14]             512\n",
      "             ReLU-98          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-99          [-1, 256, 14, 14]               0\n",
      "          Conv2d-100            [-1, 512, 7, 7]       1,179,648\n",
      "     BatchNorm2d-101            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-102            [-1, 512, 7, 7]               0\n",
      "          Conv2d-103            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-104            [-1, 512, 7, 7]           1,024\n",
      "          Conv2d-105            [-1, 512, 7, 7]         131,072\n",
      "     BatchNorm2d-106            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-107            [-1, 512, 7, 7]               0\n",
      "      BasicBlock-108            [-1, 512, 7, 7]               0\n",
      "          Conv2d-109            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-110            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-111            [-1, 512, 7, 7]               0\n",
      "          Conv2d-112            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-113            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-114            [-1, 512, 7, 7]               0\n",
      "      BasicBlock-115            [-1, 512, 7, 7]               0\n",
      "          Conv2d-116            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-117            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-118            [-1, 512, 7, 7]               0\n",
      "          Conv2d-119            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-120            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-121            [-1, 512, 7, 7]               0\n",
      "      BasicBlock-122            [-1, 512, 7, 7]               0\n",
      "     BatchNorm2d-123            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-124            [-1, 512, 7, 7]               0\n",
      "          Conv2d-125           [-1, 1024, 7, 7]       4,719,616\n",
      "            ReLU-126           [-1, 1024, 7, 7]               0\n",
      "          Conv2d-127            [-1, 512, 7, 7]       4,719,104\n",
      "            ReLU-128            [-1, 512, 7, 7]               0\n",
      "          Conv2d-129           [-1, 1024, 7, 7]         525,312\n",
      "            ReLU-130           [-1, 1024, 7, 7]               0\n",
      "    PixelShuffle-131          [-1, 256, 14, 14]               0\n",
      "PixelShuffle_ICNR-132          [-1, 256, 14, 14]               0\n",
      "     BatchNorm2d-133          [-1, 256, 14, 14]             512\n",
      "            ReLU-134          [-1, 512, 14, 14]               0\n",
      "          Conv2d-135          [-1, 512, 14, 14]       2,359,808\n",
      "            ReLU-136          [-1, 512, 14, 14]               0\n",
      "          Conv2d-137          [-1, 512, 14, 14]       2,359,808\n",
      "            ReLU-138          [-1, 512, 14, 14]               0\n",
      "       UnetBlock-139          [-1, 512, 14, 14]               0\n",
      "          Conv2d-140         [-1, 1024, 14, 14]         525,312\n",
      "            ReLU-141         [-1, 1024, 14, 14]               0\n",
      "    PixelShuffle-142          [-1, 256, 28, 28]               0\n",
      "PixelShuffle_ICNR-143          [-1, 256, 28, 28]               0\n",
      "     BatchNorm2d-144          [-1, 128, 28, 28]             256\n",
      "            ReLU-145          [-1, 384, 28, 28]               0\n",
      "          Conv2d-146          [-1, 384, 28, 28]       1,327,488\n",
      "            ReLU-147          [-1, 384, 28, 28]               0\n",
      "          Conv2d-148          [-1, 384, 28, 28]       1,327,488\n",
      "            ReLU-149          [-1, 384, 28, 28]               0\n",
      "       UnetBlock-150          [-1, 384, 28, 28]               0\n",
      "          Conv2d-151          [-1, 768, 28, 28]         295,680\n",
      "            ReLU-152          [-1, 768, 28, 28]               0\n",
      "    PixelShuffle-153          [-1, 192, 56, 56]               0\n",
      "PixelShuffle_ICNR-154          [-1, 192, 56, 56]               0\n",
      "     BatchNorm2d-155           [-1, 64, 56, 56]             128\n",
      "            ReLU-156          [-1, 256, 56, 56]               0\n",
      "          Conv2d-157          [-1, 256, 56, 56]         590,080\n",
      "            ReLU-158          [-1, 256, 56, 56]               0\n",
      "          Conv2d-159          [-1, 256, 56, 56]         590,080\n",
      "            ReLU-160          [-1, 256, 56, 56]               0\n",
      "       UnetBlock-161          [-1, 256, 56, 56]               0\n",
      "          Conv2d-162          [-1, 512, 56, 56]         131,584\n",
      "            ReLU-163          [-1, 512, 56, 56]               0\n",
      "    PixelShuffle-164        [-1, 128, 112, 112]               0\n",
      "PixelShuffle_ICNR-165        [-1, 128, 112, 112]               0\n",
      "     BatchNorm2d-166         [-1, 64, 112, 112]             128\n",
      "            ReLU-167        [-1, 192, 112, 112]               0\n",
      "          Conv2d-168         [-1, 96, 112, 112]         165,984\n",
      "            ReLU-169         [-1, 96, 112, 112]               0\n",
      "          Conv2d-170         [-1, 96, 112, 112]          83,040\n",
      "            ReLU-171         [-1, 96, 112, 112]               0\n",
      "       UnetBlock-172         [-1, 96, 112, 112]               0\n",
      "          Conv2d-173        [-1, 384, 112, 112]          37,248\n",
      "            ReLU-174        [-1, 384, 112, 112]               0\n",
      "    PixelShuffle-175         [-1, 96, 224, 224]               0\n",
      "PixelShuffle_ICNR-176         [-1, 96, 224, 224]               0\n",
      "      MergeLayer-177         [-1, 99, 224, 224]               0\n",
      "          Conv2d-178         [-1, 99, 224, 224]          88,308\n",
      "            ReLU-179         [-1, 99, 224, 224]               0\n",
      "          Conv2d-180         [-1, 99, 224, 224]          88,308\n",
      "            ReLU-181         [-1, 99, 224, 224]               0\n",
      "      MergeLayer-182         [-1, 99, 224, 224]               0\n",
      "    SequentialEx-183         [-1, 99, 224, 224]               0\n",
      "          Conv2d-184         [-1, 32, 224, 224]           3,200\n",
      "================================================================\n",
      "Total params: 41,224,168\n",
      "Trainable params: 41,224,168\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 725.62\n",
      "Params size (MB): 157.26\n",
      "Estimated Total Size (MB): 883.45\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(learn.model, input_size=(3, 224, 224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DynamicUnet(create_body(models.resnet34), n_classes=3).cuda() # use smaller resnet model here and see if that works out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 112, 112]           9,408\n",
      "       BatchNorm2d-2         [-1, 64, 112, 112]             128\n",
      "              ReLU-3         [-1, 64, 112, 112]               0\n",
      "         MaxPool2d-4           [-1, 64, 56, 56]               0\n",
      "            Conv2d-5           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-6           [-1, 64, 56, 56]             128\n",
      "              ReLU-7           [-1, 64, 56, 56]               0\n",
      "            Conv2d-8           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-9           [-1, 64, 56, 56]             128\n",
      "             ReLU-10           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-11           [-1, 64, 56, 56]               0\n",
      "           Conv2d-12           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-13           [-1, 64, 56, 56]             128\n",
      "             ReLU-14           [-1, 64, 56, 56]               0\n",
      "           Conv2d-15           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
      "             ReLU-17           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-18           [-1, 64, 56, 56]               0\n",
      "           Conv2d-19           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-20           [-1, 64, 56, 56]             128\n",
      "             ReLU-21           [-1, 64, 56, 56]               0\n",
      "           Conv2d-22           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-23           [-1, 64, 56, 56]             128\n",
      "             ReLU-24           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-25           [-1, 64, 56, 56]               0\n",
      "           Conv2d-26          [-1, 128, 28, 28]          73,728\n",
      "      BatchNorm2d-27          [-1, 128, 28, 28]             256\n",
      "             ReLU-28          [-1, 128, 28, 28]               0\n",
      "           Conv2d-29          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-30          [-1, 128, 28, 28]             256\n",
      "           Conv2d-31          [-1, 128, 28, 28]           8,192\n",
      "      BatchNorm2d-32          [-1, 128, 28, 28]             256\n",
      "             ReLU-33          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-34          [-1, 128, 28, 28]               0\n",
      "           Conv2d-35          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-36          [-1, 128, 28, 28]             256\n",
      "             ReLU-37          [-1, 128, 28, 28]               0\n",
      "           Conv2d-38          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-39          [-1, 128, 28, 28]             256\n",
      "             ReLU-40          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-41          [-1, 128, 28, 28]               0\n",
      "           Conv2d-42          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-43          [-1, 128, 28, 28]             256\n",
      "             ReLU-44          [-1, 128, 28, 28]               0\n",
      "           Conv2d-45          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-46          [-1, 128, 28, 28]             256\n",
      "             ReLU-47          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-48          [-1, 128, 28, 28]               0\n",
      "           Conv2d-49          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-50          [-1, 128, 28, 28]             256\n",
      "             ReLU-51          [-1, 128, 28, 28]               0\n",
      "           Conv2d-52          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-53          [-1, 128, 28, 28]             256\n",
      "             ReLU-54          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-55          [-1, 128, 28, 28]               0\n",
      "           Conv2d-56          [-1, 256, 14, 14]         294,912\n",
      "      BatchNorm2d-57          [-1, 256, 14, 14]             512\n",
      "             ReLU-58          [-1, 256, 14, 14]               0\n",
      "           Conv2d-59          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-60          [-1, 256, 14, 14]             512\n",
      "           Conv2d-61          [-1, 256, 14, 14]          32,768\n",
      "      BatchNorm2d-62          [-1, 256, 14, 14]             512\n",
      "             ReLU-63          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-64          [-1, 256, 14, 14]               0\n",
      "           Conv2d-65          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-66          [-1, 256, 14, 14]             512\n",
      "             ReLU-67          [-1, 256, 14, 14]               0\n",
      "           Conv2d-68          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-69          [-1, 256, 14, 14]             512\n",
      "             ReLU-70          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-71          [-1, 256, 14, 14]               0\n",
      "           Conv2d-72          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-73          [-1, 256, 14, 14]             512\n",
      "             ReLU-74          [-1, 256, 14, 14]               0\n",
      "           Conv2d-75          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-76          [-1, 256, 14, 14]             512\n",
      "             ReLU-77          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-78          [-1, 256, 14, 14]               0\n",
      "           Conv2d-79          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-80          [-1, 256, 14, 14]             512\n",
      "             ReLU-81          [-1, 256, 14, 14]               0\n",
      "           Conv2d-82          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-83          [-1, 256, 14, 14]             512\n",
      "             ReLU-84          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-85          [-1, 256, 14, 14]               0\n",
      "           Conv2d-86          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-87          [-1, 256, 14, 14]             512\n",
      "             ReLU-88          [-1, 256, 14, 14]               0\n",
      "           Conv2d-89          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-90          [-1, 256, 14, 14]             512\n",
      "             ReLU-91          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-92          [-1, 256, 14, 14]               0\n",
      "           Conv2d-93          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-94          [-1, 256, 14, 14]             512\n",
      "             ReLU-95          [-1, 256, 14, 14]               0\n",
      "           Conv2d-96          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-97          [-1, 256, 14, 14]             512\n",
      "             ReLU-98          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-99          [-1, 256, 14, 14]               0\n",
      "          Conv2d-100            [-1, 512, 7, 7]       1,179,648\n",
      "     BatchNorm2d-101            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-102            [-1, 512, 7, 7]               0\n",
      "          Conv2d-103            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-104            [-1, 512, 7, 7]           1,024\n",
      "          Conv2d-105            [-1, 512, 7, 7]         131,072\n",
      "     BatchNorm2d-106            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-107            [-1, 512, 7, 7]               0\n",
      "      BasicBlock-108            [-1, 512, 7, 7]               0\n",
      "          Conv2d-109            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-110            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-111            [-1, 512, 7, 7]               0\n",
      "          Conv2d-112            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-113            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-114            [-1, 512, 7, 7]               0\n",
      "      BasicBlock-115            [-1, 512, 7, 7]               0\n",
      "          Conv2d-116            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-117            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-118            [-1, 512, 7, 7]               0\n",
      "          Conv2d-119            [-1, 512, 7, 7]       2,359,296\n",
      "     BatchNorm2d-120            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-121            [-1, 512, 7, 7]               0\n",
      "      BasicBlock-122            [-1, 512, 7, 7]               0\n",
      "     BatchNorm2d-123            [-1, 512, 7, 7]           1,024\n",
      "            ReLU-124            [-1, 512, 7, 7]               0\n",
      "          Conv2d-125           [-1, 1024, 7, 7]       4,718,592\n",
      "            ReLU-126           [-1, 1024, 7, 7]               0\n",
      "     BatchNorm2d-127           [-1, 1024, 7, 7]           2,048\n",
      "          Conv2d-128            [-1, 512, 7, 7]       4,718,592\n",
      "            ReLU-129            [-1, 512, 7, 7]               0\n",
      "     BatchNorm2d-130            [-1, 512, 7, 7]           1,024\n",
      "          Conv2d-131           [-1, 1024, 7, 7]         525,312\n",
      "            ReLU-132           [-1, 1024, 7, 7]               0\n",
      "    PixelShuffle-133          [-1, 256, 14, 14]               0\n",
      "PixelShuffle_ICNR-134          [-1, 256, 14, 14]               0\n",
      "     BatchNorm2d-135          [-1, 256, 14, 14]             512\n",
      "            ReLU-136          [-1, 512, 14, 14]               0\n",
      "          Conv2d-137          [-1, 512, 14, 14]       2,359,296\n",
      "            ReLU-138          [-1, 512, 14, 14]               0\n",
      "     BatchNorm2d-139          [-1, 512, 14, 14]           1,024\n",
      "       UnetBlock-140          [-1, 512, 14, 14]               0\n",
      "          Conv2d-141         [-1, 1024, 14, 14]         525,312\n",
      "            ReLU-142         [-1, 1024, 14, 14]               0\n",
      "    PixelShuffle-143          [-1, 256, 28, 28]               0\n",
      "PixelShuffle_ICNR-144          [-1, 256, 28, 28]               0\n",
      "     BatchNorm2d-145          [-1, 128, 28, 28]             256\n",
      "            ReLU-146          [-1, 384, 28, 28]               0\n",
      "          Conv2d-147          [-1, 384, 28, 28]       1,327,104\n",
      "            ReLU-148          [-1, 384, 28, 28]               0\n",
      "     BatchNorm2d-149          [-1, 384, 28, 28]             768\n",
      "       UnetBlock-150          [-1, 384, 28, 28]               0\n",
      "          Conv2d-151          [-1, 768, 28, 28]         295,680\n",
      "            ReLU-152          [-1, 768, 28, 28]               0\n",
      "    PixelShuffle-153          [-1, 192, 56, 56]               0\n",
      "PixelShuffle_ICNR-154          [-1, 192, 56, 56]               0\n",
      "     BatchNorm2d-155           [-1, 64, 56, 56]             128\n",
      "            ReLU-156          [-1, 256, 56, 56]               0\n",
      "          Conv2d-157          [-1, 256, 56, 56]         589,824\n",
      "            ReLU-158          [-1, 256, 56, 56]               0\n",
      "     BatchNorm2d-159          [-1, 256, 56, 56]             512\n",
      "       UnetBlock-160          [-1, 256, 56, 56]               0\n",
      "          Conv2d-161          [-1, 512, 56, 56]         131,584\n",
      "            ReLU-162          [-1, 512, 56, 56]               0\n",
      "    PixelShuffle-163        [-1, 128, 112, 112]               0\n",
      "PixelShuffle_ICNR-164        [-1, 128, 112, 112]               0\n",
      "     BatchNorm2d-165         [-1, 64, 112, 112]             128\n",
      "            ReLU-166        [-1, 192, 112, 112]               0\n",
      "          Conv2d-167         [-1, 96, 112, 112]         165,888\n",
      "            ReLU-168         [-1, 96, 112, 112]               0\n",
      "     BatchNorm2d-169         [-1, 96, 112, 112]             192\n",
      "       UnetBlock-170         [-1, 96, 112, 112]               0\n",
      "          Conv2d-171        [-1, 384, 112, 112]          37,248\n",
      "            ReLU-172        [-1, 384, 112, 112]               0\n",
      "    PixelShuffle-173         [-1, 96, 224, 224]               0\n",
      "PixelShuffle_ICNR-174         [-1, 96, 224, 224]               0\n",
      "      MergeLayer-175         [-1, 99, 224, 224]               0\n",
      "          Conv2d-176         [-1, 99, 224, 224]          88,209\n",
      "            ReLU-177         [-1, 99, 224, 224]               0\n",
      "     BatchNorm2d-178         [-1, 99, 224, 224]             198\n",
      "          Conv2d-179         [-1, 99, 224, 224]          88,209\n",
      "            ReLU-180         [-1, 99, 224, 224]               0\n",
      "     BatchNorm2d-181         [-1, 99, 224, 224]             198\n",
      "      MergeLayer-182         [-1, 99, 224, 224]               0\n",
      "    SequentialEx-183         [-1, 99, 224, 224]               0\n",
      "          Conv2d-184          [-1, 3, 224, 224]             297\n",
      "     BatchNorm2d-185          [-1, 3, 224, 224]               6\n",
      "================================================================\n",
      "Total params: 36,863,837\n",
      "Trainable params: 36,863,837\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 773.66\n",
      "Params size (MB): 140.62\n",
      "Estimated Total Size (MB): 914.86\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, input_size=(3, 224, 224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet18-5c106cde.pth\" to /home/navid/.cache/torch/checkpoints/resnet18-5c106cde.pth\n",
      "100%|██████████| 46827520/46827520 [00:04<00:00, 11219550.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 112, 112]           9,408\n",
      "       BatchNorm2d-2         [-1, 64, 112, 112]             128\n",
      "              ReLU-3         [-1, 64, 112, 112]               0\n",
      "         MaxPool2d-4           [-1, 64, 56, 56]               0\n",
      "            Conv2d-5           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-6           [-1, 64, 56, 56]             128\n",
      "              ReLU-7           [-1, 64, 56, 56]               0\n",
      "            Conv2d-8           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-9           [-1, 64, 56, 56]             128\n",
      "             ReLU-10           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-11           [-1, 64, 56, 56]               0\n",
      "           Conv2d-12           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-13           [-1, 64, 56, 56]             128\n",
      "             ReLU-14           [-1, 64, 56, 56]               0\n",
      "           Conv2d-15           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
      "             ReLU-17           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-18           [-1, 64, 56, 56]               0\n",
      "           Conv2d-19          [-1, 128, 28, 28]          73,728\n",
      "      BatchNorm2d-20          [-1, 128, 28, 28]             256\n",
      "             ReLU-21          [-1, 128, 28, 28]               0\n",
      "           Conv2d-22          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-23          [-1, 128, 28, 28]             256\n",
      "           Conv2d-24          [-1, 128, 28, 28]           8,192\n",
      "      BatchNorm2d-25          [-1, 128, 28, 28]             256\n",
      "             ReLU-26          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-27          [-1, 128, 28, 28]               0\n",
      "           Conv2d-28          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-29          [-1, 128, 28, 28]             256\n",
      "             ReLU-30          [-1, 128, 28, 28]               0\n",
      "           Conv2d-31          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-32          [-1, 128, 28, 28]             256\n",
      "             ReLU-33          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-34          [-1, 128, 28, 28]               0\n",
      "           Conv2d-35          [-1, 256, 14, 14]         294,912\n",
      "      BatchNorm2d-36          [-1, 256, 14, 14]             512\n",
      "             ReLU-37          [-1, 256, 14, 14]               0\n",
      "           Conv2d-38          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-39          [-1, 256, 14, 14]             512\n",
      "           Conv2d-40          [-1, 256, 14, 14]          32,768\n",
      "      BatchNorm2d-41          [-1, 256, 14, 14]             512\n",
      "             ReLU-42          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-43          [-1, 256, 14, 14]               0\n",
      "           Conv2d-44          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-45          [-1, 256, 14, 14]             512\n",
      "             ReLU-46          [-1, 256, 14, 14]               0\n",
      "           Conv2d-47          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-48          [-1, 256, 14, 14]             512\n",
      "             ReLU-49          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-50          [-1, 256, 14, 14]               0\n",
      "           Conv2d-51            [-1, 512, 7, 7]       1,179,648\n",
      "      BatchNorm2d-52            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-53            [-1, 512, 7, 7]               0\n",
      "           Conv2d-54            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-55            [-1, 512, 7, 7]           1,024\n",
      "           Conv2d-56            [-1, 512, 7, 7]         131,072\n",
      "      BatchNorm2d-57            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-58            [-1, 512, 7, 7]               0\n",
      "       BasicBlock-59            [-1, 512, 7, 7]               0\n",
      "           Conv2d-60            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-61            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-62            [-1, 512, 7, 7]               0\n",
      "           Conv2d-63            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-64            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-65            [-1, 512, 7, 7]               0\n",
      "       BasicBlock-66            [-1, 512, 7, 7]               0\n",
      "      BatchNorm2d-67            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-68            [-1, 512, 7, 7]               0\n",
      "           Conv2d-69           [-1, 1024, 7, 7]       4,719,616\n",
      "             ReLU-70           [-1, 1024, 7, 7]               0\n",
      "           Conv2d-71            [-1, 512, 7, 7]       4,719,104\n",
      "             ReLU-72            [-1, 512, 7, 7]               0\n",
      "           Conv2d-73           [-1, 1024, 7, 7]         525,312\n",
      "             ReLU-74           [-1, 1024, 7, 7]               0\n",
      "     PixelShuffle-75          [-1, 256, 14, 14]               0\n",
      "PixelShuffle_ICNR-76          [-1, 256, 14, 14]               0\n",
      "      BatchNorm2d-77          [-1, 256, 14, 14]             512\n",
      "             ReLU-78          [-1, 512, 14, 14]               0\n",
      "           Conv2d-79          [-1, 512, 14, 14]       2,359,808\n",
      "             ReLU-80          [-1, 512, 14, 14]               0\n",
      "           Conv2d-81          [-1, 512, 14, 14]       2,359,808\n",
      "             ReLU-82          [-1, 512, 14, 14]               0\n",
      "        UnetBlock-83          [-1, 512, 14, 14]               0\n",
      "           Conv2d-84         [-1, 1024, 14, 14]         525,312\n",
      "             ReLU-85         [-1, 1024, 14, 14]               0\n",
      "     PixelShuffle-86          [-1, 256, 28, 28]               0\n",
      "PixelShuffle_ICNR-87          [-1, 256, 28, 28]               0\n",
      "      BatchNorm2d-88          [-1, 128, 28, 28]             256\n",
      "             ReLU-89          [-1, 384, 28, 28]               0\n",
      "           Conv2d-90          [-1, 384, 28, 28]       1,327,488\n",
      "             ReLU-91          [-1, 384, 28, 28]               0\n",
      "           Conv2d-92          [-1, 384, 28, 28]       1,327,488\n",
      "             ReLU-93          [-1, 384, 28, 28]               0\n",
      "        UnetBlock-94          [-1, 384, 28, 28]               0\n",
      "           Conv2d-95          [-1, 768, 28, 28]         295,680\n",
      "             ReLU-96          [-1, 768, 28, 28]               0\n",
      "     PixelShuffle-97          [-1, 192, 56, 56]               0\n",
      "PixelShuffle_ICNR-98          [-1, 192, 56, 56]               0\n",
      "      BatchNorm2d-99           [-1, 64, 56, 56]             128\n",
      "            ReLU-100          [-1, 256, 56, 56]               0\n",
      "          Conv2d-101          [-1, 256, 56, 56]         590,080\n",
      "            ReLU-102          [-1, 256, 56, 56]               0\n",
      "          Conv2d-103          [-1, 256, 56, 56]         590,080\n",
      "            ReLU-104          [-1, 256, 56, 56]               0\n",
      "       UnetBlock-105          [-1, 256, 56, 56]               0\n",
      "          Conv2d-106          [-1, 512, 56, 56]         131,584\n",
      "            ReLU-107          [-1, 512, 56, 56]               0\n",
      "    PixelShuffle-108        [-1, 128, 112, 112]               0\n",
      "PixelShuffle_ICNR-109        [-1, 128, 112, 112]               0\n",
      "     BatchNorm2d-110         [-1, 64, 112, 112]             128\n",
      "            ReLU-111        [-1, 192, 112, 112]               0\n",
      "          Conv2d-112         [-1, 96, 112, 112]         165,984\n",
      "            ReLU-113         [-1, 96, 112, 112]               0\n",
      "          Conv2d-114         [-1, 96, 112, 112]          83,040\n",
      "            ReLU-115         [-1, 96, 112, 112]               0\n",
      "       UnetBlock-116         [-1, 96, 112, 112]               0\n",
      "          Conv2d-117        [-1, 384, 112, 112]          37,248\n",
      "            ReLU-118        [-1, 384, 112, 112]               0\n",
      "    PixelShuffle-119         [-1, 96, 224, 224]               0\n",
      "PixelShuffle_ICNR-120         [-1, 96, 224, 224]               0\n",
      "      MergeLayer-121         [-1, 99, 224, 224]               0\n",
      "          Conv2d-122         [-1, 99, 224, 224]          88,308\n",
      "            ReLU-123         [-1, 99, 224, 224]               0\n",
      "          Conv2d-124         [-1, 99, 224, 224]          88,308\n",
      "            ReLU-125         [-1, 99, 224, 224]               0\n",
      "      MergeLayer-126         [-1, 99, 224, 224]               0\n",
      "    SequentialEx-127         [-1, 99, 224, 224]               0\n",
      "          Conv2d-128         [-1, 32, 224, 224]           3,200\n",
      "================================================================\n",
      "Total params: 31,116,008\n",
      "Trainable params: 31,116,008\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 692.12\n",
      "Params size (MB): 118.70\n",
      "Estimated Total Size (MB): 811.40\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "learn_small = unet_learner(data, models.resnet18, metrics=metrics, wd=wd)\n",
    "learn_small.unfreeze()\n",
    "\n",
    "summary(learn_small.model, input_size=(3, 224, 224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DynamicUnet(\n",
       "  (layers): ModuleList(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace)\n",
       "      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      (4): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (5): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (6): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (7): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Sequential(\n",
       "      (0): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (1): Sequential(\n",
       "        (0): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "    )\n",
       "    (4): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (shuf): PixelShuffle(upscale_factor=2)\n",
       "        (pad): ReplicationPad2d((1, 0, 1, 0))\n",
       "        (blur): AvgPool2d(kernel_size=2, stride=1, padding=0)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (5): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (shuf): PixelShuffle(upscale_factor=2)\n",
       "        (pad): ReplicationPad2d((1, 0, 1, 0))\n",
       "        (blur): AvgPool2d(kernel_size=2, stride=1, padding=0)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (6): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2d(384, 768, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (shuf): PixelShuffle(upscale_factor=2)\n",
       "        (pad): ReplicationPad2d((1, 0, 1, 0))\n",
       "        (blur): AvgPool2d(kernel_size=2, stride=1, padding=0)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (7): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (conv): Sequential(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "        )\n",
       "        (shuf): PixelShuffle(upscale_factor=2)\n",
       "        (pad): ReplicationPad2d((1, 0, 1, 0))\n",
       "        (blur): AvgPool2d(kernel_size=2, stride=1, padding=0)\n",
       "        (relu): ReLU(inplace)\n",
       "      )\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): Sequential(\n",
       "        (0): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (conv2): Sequential(\n",
       "        (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU(inplace)\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (8): PixelShuffle_ICNR(\n",
       "      (conv): Sequential(\n",
       "        (0): Conv2d(96, 384, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (shuf): PixelShuffle(upscale_factor=2)\n",
       "      (pad): ReplicationPad2d((1, 0, 1, 0))\n",
       "      (blur): AvgPool2d(kernel_size=2, stride=1, padding=0)\n",
       "      (relu): ReLU(inplace)\n",
       "    )\n",
       "    (9): MergeLayer()\n",
       "    (10): SequentialEx(\n",
       "      (layers): ModuleList(\n",
       "        (0): Sequential(\n",
       "          (0): Conv2d(99, 99, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU(inplace)\n",
       "        )\n",
       "        (1): Sequential(\n",
       "          (0): Conv2d(99, 99, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU(inplace)\n",
       "        )\n",
       "        (2): MergeLayer()\n",
       "      )\n",
       "    )\n",
       "    (11): Sequential(\n",
       "      (0): Conv2d(99, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn_small.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "FASTAI",
   "language": "python",
   "name": "fastai"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
